Final answer:
In demand forecasting using regression analysis, the relationship between dependent variables (like sales) and independent variables (such as price, income, advertising) is ascertained. A scatter plot is drawn to visually assess this relationship, and regression is used to find the line of best fit and calculate the correlation coefficient, which indicates the strength and direction of their relationship.
Step-by-step explanation:
When conducting demand forecasting using regression analysis, one would establish the relationship between a dependent variable, such as sales, and various independent variables, which could include factors like the price of related goods, income levels, and expenditure on advertisement. The dependent variable is the outcome that you're trying to predict or explain, while the independent variables are the inputs you believe will have an effect on that outcome.
A typical starting point is to draw a scatter plot to visually examine the relationship between these variables. Then, using regression, analysts find the line of best fit, which is the line that minimizes the distance between itself and each point on the scatter plot. Further, they’ll calculate the correlation coefficient, a value that quantifies the strength and direction of the relationship between the variables.
To assess the nature of the relationship, one would interpret the significance of the correlation coefficient. A significant coefficient implies that there is a noteworthy relationship between the variables. Lastly, one must evaluate whether the relationship appears to be linear, meaning as one variable increases or decreases, the other variable also changes in a consistent manner.